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Machine learning models can handle heterogeneous datasets and complex relationships.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/using-machine-learning-to-predict-adverse-effects-of-metallic-nanomaterials-to-various-aquatic-organisms/291179/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/using-machine-learning-to-predict-adverse-effects-of-metallic-nanomaterials-to-various-aquatic-organisms/291179.png","ImageObject",300,407,{"name":42,"@type":43},"Aurelia","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-06","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why are experimental ecotoxicity studies of metallic nanomaterials difficult?","Question",{"text":63,"@type":64},"They are time-consuming and expensive due to multiple environmental factors, complex material properties, and diverse species responses.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What machine learning approach does the study propose?",{"text":68,"@type":64},"It establishes an in silico model (ML-PEMST) using machine learning properties, environmental conditions, multi-species toxicity prediction to forecast toxicity across MNMs and aquatic species.",{"name":70,"@type":61,"acceptedAnswer":71},"Which factors were identified as main drivers of ecotoxicity?",{"text":72,"@type":64},"Random forest feature importance and interaction analysis indicated exposure duration, illumination, primary size, and hydrodynamic diameter as key factors, with illumination showing the strongest interactions with other features.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},291179,1789643603,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":55,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},1099514068365,"https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1794309523707217728","Using machine learning to predict adverse effects of metallic nanomaterials to various aquatic organisms  \nZhou, Y.; Wang, Y.; Peijnenburg, W.J.G.M.; Vijver, M.G.; Balraadjsing, S.; Fan, W.  \nCitation  \nZhou, Y., Wang, Y., Peijnenburg, W. J. G. M., Vijver, M. G., Balraadjsing, S., & Fan, W. (2023) . Using machine learning to predict adverse effects of metallic nanomaterials to various aquatic organisms. Environmental Science And Technology. doi:10.1021/acs.est.2c07039  \nVersion: Publisher's Version  \nLicense:  Licensed under Article 25fa Copyright Act/Law (Amendment Taverne)  \nDownloaded from:  [https://hdl.handle.net/1887/3594144](https://hdl.handle.net/1887/3594144)  \nNote: To cite this publication please use the final published version (if applicable) .  \n[pubs.acs.org/est](pubs.acs.org/est)  Article   \nUsing Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms  \nYunchi Zhou, Ying Wang,* Willie Peijnenburg, Martina G. Vijver, Surendra Balraadjsing, and Wenhong Fan*  \n Cite This: [https://doi.org/10.1021/acs.est.2c07039](https://doi.org/10.1021/acs.est.2c07039)  \nRead Online  \nDownloaded via LEIDEN UNIV on April 18, 2023 at 09:11:38 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nACCESS  \n Metrics & More  \n Article Recommendations  \n*sı   \nSupporting Information  \nABSTRACT: The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random  \nforest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.  \nKEYWORDS: nanomaterials, toxicity, machine learning, prediction, exposure conditions, aquatic organisms  \n■ INTRODUCTION  \nMetallic nanomaterials (MNMs) are widely used in many fields because of their outstanding physical and chemical properties.1 The increasing applications of MNMs in industries and society have caused a rapid growth of the production volume of NMs,2 of which 80% are metals or metal oxides with an annual production of approximately 242,000 tons.2,3 MNMs released into the aquatic environment will pose risks to aquatic organisms like fish, crustacean, algae, bacteria.4,5 The exposure to MNMs has been proved to cause the growth inhibition of algae,6 the immobilization and genotoxic effect of Daphnia magna (D. magna),7 and the cytotoxicity of fish cells.8 Numerous studies proved that the toxicity varies with properties such as size,9 shape, 10 surface area, and surface composition.11 Moreover, due to the complex properties of different MNMs, a slight change in shape and size may significantly affect their toxic effect in aquatic environment.12, 13 Given the complexity of the aq","cbCaipG2MkaQ0ap2","https://ap.wps.com/l/cbCaipG2MkaQ0ap2","pdf",7631951,11,"English","# Abstract\n## Introduction\n## Background on nano-QSARs and related modeling approaches","[{\"question\":\"Why are experimental ecotoxicity studies of metallic nanomaterials difficult?\",\"answer\":\"They are time-consuming and expensive due to multiple environmental factors, complex material properties, and diverse species responses.\"},{\"question\":\"What machine learning approach does the study propose?\",\"answer\":\"It establishes an in silico model (ML-PEMST) using machine learning properties, environmental conditions, multi-species toxicity prediction to forecast toxicity across MNMs and aquatic species.\"},{\"question\":\"Which factors were identified as main drivers of ecotoxicity?\",\"answer\":\"Random forest feature importance and interaction analysis indicated exposure duration, illumination, primary size, and hydrodynamic diameter as key factors, with illumination showing the strongest interactions with other features.\"}]","Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms | PDF",28]